Bayesian federated inference for survival models

Hassan Pazira, Emanuele Massa, Jetty A.M. Weijers, Anthony C.C. Coolen and Marianne A. Jonker

Journal of Applied Statistics, 2026, vol. 53, issue 2, 203-223

Abstract: To accurately estimate the parameters in a prediction model for survival data, sufficient events need to be observed compared to the number of model parameters. In practice, this is often a problem. Merging data sets from different medical centers may help, but this is not always possible due to strict privacy legislation and logistic difficulties. Recently, the Bayesian Federated Inference (BFI) strategy for generalized linear models was proposed. With this strategy, the statistical analyzes are performed in the local centers where the data were collected (or stored), and only the inference results are combined to a single estimated model; merging data is not necessary. The BFI methodology aims to compute from the separate inference results in the local centers what would have been obtained if the analysis had been based on the merged data sets. In the present paper, we generalize the BFI methodology as initially developed for generalized linear models to survival models. Simulation studies and real data analyzes show excellent performance; that is, the results obtained with the BFI methodology are very similar to the results obtained by analyzing the merged data. An R package for doing the analyzes is available.

Date: 2026
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DOI: 10.1080/02664763.2025.2511932

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